Multi-level alignment hints based on conversational dynamics improve scam detection accuracy and support earlier confidence formation compared with keyword alerts.
Sok: a comprehensive reexamination of phishing research from the security perspective,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
PHISHREV integrates ML classifiers with ASP-based non-monotonic reasoning to revise 5.08% of phishing predictions for improved consistency and allows O(n) incorporation of new knowledge without retraining.
citing papers explorer
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Making Sense of Scams: Understanding Scam Conversations Through Multi-Level Alignment
Multi-level alignment hints based on conversational dynamics improve scam detection accuracy and support earlier confidence formation compared with keyword alerts.
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PHISHREV: A Hybrid Machine Learning and Post-Hoc Non-monotonic Reasoning Framework for Context-Aware Phishing Website Classification
PHISHREV integrates ML classifiers with ASP-based non-monotonic reasoning to revise 5.08% of phishing predictions for improved consistency and allows O(n) incorporation of new knowledge without retraining.